Abstract

Reheating furnace is an important thermal technology equipment in the process of billet rolling. The level of operation has great effect on the norm of quality, output, production and consumption. It provides qualified billets for hot-rolling process. However, it is difficult to measure the temperature of billet during production. So it is necessary to build a reasonable model to predict temperature.The billet’s reheating furnace is a typical complicated industry system with seriously lag-lead, multivariable, seriously coupled, time-variety, non-linear, pure time delay and a lot of disturbance factor. The billet’s temperature can not be measured online. According to the walking beam reheating furnace in this thesis, T-S model is applied to the research of the billet temperature forecasting. The model’s structure and parameters are identified apart. At first, fuzzy c-means (FCM) is used to identify antecedent parameter and structure. Then, consequent parameter is identified by least square. In order to improve the model’s precision, fuzzy-neural network based on T-S model that combines subtractive clustering and the FCM is developed in the thesis. FCM algorithm is sensitive to its initial value and liable to be trapped in a local minimum. So subtractive clustering is used to determine the initial clustering center and then FCM is used to improve the clustering’s convergence speed. Then a mixed Particle Swarm Optimizer-Back Propagation algorithm is presented to identify consequent parameter. At last, the model is simulated by MATLAB using the data gathering on-site.The simulation results have proved that the improved fuzzy-neural network based on T-S model is better in predicting the billet temperatures. If the number of rules and parameters is certain, T-S fuzzy model based on FCM and least square can identify parameter according to the only mean square error. When we want to improve the model precision, we must increase rules. However, the improved model based on PSO-BP algorithm can identify the parameters according to any mean square error in a range. This is because the mixed PSO-BP algorithm is random in essence. This model can improve the precision without increase rules, so it is more suitable for engineering. According to the temperature of billet, workers can control reheating furnace timely so that we can guarantee the quality of the billet. The research of this thesis has not only theoretical significance, but also practical application value.